Multi-hypervisor virtual machines

ABSTRACT

Standard nested virtualization allows a hypervisor to run other hypervisors as guests, i.e. a level-0 (L0) hypervisor can run multiple level-1 (L1) hypervisors, each of which can run multiple level-2 (L2) virtual machines (VMs), with each L2 VM is restricted to run on only one L1 hypervisor. Span provides a Multi-hypervisor VM in which a single VM can simultaneously run on multiple hypervisors, which permits a VM to benefit from different services provided by multiple hypervisors that co-exist on a single physical machine. Span allows (a) the memory footprint of the VM to be shared across two hypervisors, and (b) the responsibility for CPU and I/O scheduling to be distributed among the two hypervisors. Span VMs can achieve performance comparable to traditional (single-hypervisor) nested VMs for common benchmarks.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is Continuation of U.S. patent application Ser. No. 15/970,751, filed Oct. 23, 2017, now U.S. Pat. No. 10,437,627, issued Oct. 8, 2019, which is a Continuation of U.S. patent application Ser. No. 14/947,595, filed Nov. 20, 2015, now U.S. Pat. No. 9,798,567, issued Oct. 24, 2017, which is a non-provisional of, and claims benefit of priority from, U.S. Provisional Patent Application No. 62/084,489, filed Nov. 25, 2014, the entirety of which are each expressly incorporated herein by reference.

STATEMENT OF GOVERNMENT SUPPORT

This work was made with government support under CNS-0845832, CNS-1320689, and CNS-1527338. The government has certain rights in this invention.

BACKGROUND OF THE INVENTION

In traditional, or single-level, machine virtualization a hypervisor controls the hardware (bare-metal) resources and runs one or more concurrent virtual machines (VMs), each VM running its own guest operating system. Nested virtualization enables a bare-metal hypervisor (level-0 or L0) to run one or more hypervisors (level-1 or L1), each of which can run its own set of VMs [18, 7, 29, 13] (level-2 or L2). Nested virtualization has many known potential benefits [7]. It can be used to host VMs running commodity operating systems, such as Linux and Windows, that utilize hardware virtualization to host other operating systems. Hypervisors that are embedded in firmware [15, 31] could use virtualization to run other hypervisors. Infrastructure-as-a-Service (IaaS) providers could use nested virtualization to allow users to run their own hypervisors and to allow migration of VMs across different IaaS providers [45]. Nested virtualization could also allow new approaches to hypervisor-level security [35, 33, 37, 20, 21, 14, 4], hypervisor development, and testing.

Besides the above benefits, nested virtualization also opens up a new possibility. L1 hypervisors that provide different services could be co-located on the same machine. An L2 VM according to the present technology could simultaneously use these diverse L1 services. For instance, besides running on a commodity L1 hypervisor, an L2 VM could simultaneously run on another L1 hypervisor that provides an intrusion detection service, or a deduplication [46] service, or a real-time CPU or I/O scheduling service.

Unfortunately, current nested virtualization solutions restrict an L2 VM to run on only one L1 hypervisor at a time. This prevents an L2 VM from taking advantage of services from multiple L1 hypervisors.

Nested VMs were originally proposed and refined in [16, 17, 32, 5, 6]. IBM z/VM [29] was the first implementation of nested VMs using multiple levels of hardware support for nested virtualization. Ford et al. [13] implemented nested VMs in a microkernel environment. Graf and Roedel [18] and Ben-Yehuda et al. [7] implemented nested VM support in the KVM [23] hypervisor on AMDV [1] and Intel VMX [42] platforms respectively. Unlike IBM z/VM, these rely on only a single level of hardware virtualization support. Prior nested VM platforms restrict the L2 VM to execute on a single L1 hypervisor at a time. Although one can technically live migrate [11, 19] an L2 VM from one L1 hypervisor to another, the “one-hypervisor-at-a-time” restriction still applies. None of the prior approaches allow a single L2 VM to execute simultaneously on multiple L1 hypervisors on the same physical machine.

Distributed operating systems, such as Amoeba [36, 2] and Sprite [22], aim to aggregate the resources of multiple networked machines into a single pool. ScaleMP [43] is a commercial system that provides a distributed hypervisor spanning multiple physical machines, to transparently support SMP VMs, and also supports nested VMs via a feature called VM-on-VM, but does not appear to support multi-hypervisor VMs. Further, being a proprietary product, very few implementation details are available. DVM [38] implements a distributed virtual machine service for the Java platform by moving system services such as verification, security enforcement, compilation and optimization, out of the client into central servers. In contrast to such systems that aggregate resources across multiple physical machines, the present technology, called Span, transparently supports nested VMs that span multiple co-located L1 hypervisors.

A related line of research relates to dis-aggregating the large administrative domain [25, 12, 10, 40] typically associated with a hypervisor, such as Domain 0 in Xen. The goal of these efforts is to replace a single large administrative domain with several small sub-domains (akin to privileged service-VMs) that are more resilient to attacks and failures, better isolated from others, and can be customized on a per-VM basis. Thus a VM could pick and choose the services of specific sub-domains which run at the same level as the VM atop the common hypervisor. In contrast to prior efforts, the present technology supports running a VM simultaneously on multiple lower-level hypervisors, each of which could possibly offer specialized hypervisor-level services.

As only L0 can execute in the highest privileged mode, all privileged instructions executed by L1 and L2 are trapped by L0. This same hierarchical constraint would generally apply to a deeper set of hypervisors: each hypervisor can execute with no further privilege than its parent, and typically, certain privileges are reserved to the parent or L0 and denied to the child, thus functionally distinguishing the layers.

SUMMARY OF THE INVENTION

The present technology provides a multi-hypervisor virtual machine (MHVM) that enables a VM to simultaneously execute on multiple co-located hypervisors by leveraging virtualization.

The present technology enables cloud providers to co-locate multiple third-party hypervisors that provide different services on the same physical machine. A VM can thus simultaneously use the diverse L1 services such as VM introspection, intrusion detection, deduplication, or real-time CPU or I/O scheduling. A new cloud architecture is provided in which cloud providers can enable third parties to execute multiple-independently developed or maintained-hypervisors, each contributing different features. Indeed, because a VM can employ multiple hypervisors, new hypervisor may be provided which provides only new functions, and may rely on another hypervisor platform or platforms for complete support of execution by the VM. Therefore, VMs may be modular, and may be provided as a set of optional alternates.

Lean hypervisors are therefore possible that specialize in providing specific services. VMs could then pick and choose any (and only the) hypervisors they need.

Even hypervisors from a single source may have different versions, which may impose compatibility issues with respect to legacy code. Therefore, the present technology permits these various hypervisors to coexist and concurrently operate.

A multi-hypervisor virtual machine is provided, according to the present technology, as an L2 VM that can simultaneously run on multiple hypervisors. FIG. 1 shows a high-level illustration of various possibilities. A single L0 hypervisor runs multiple L1 hypervisors (H1, H2, H3, and H4) and multiple L2 VMs (V1, V2, V3 and V4). V1 is a traditional nested VM that runs on only one hypervisor (H1). The rest are multi-hypervisor VMs. V2 runs on two hypervisors (H1 and H2). V3 runs on three hypervisors (H2, H3, and H4). V4 runs in a hybrid mode on H4 and L0.

A multi-hypervisor VM, e.g., a L2 VM, is considered to simultaneously “run” on multiple L1 hypervisors when the underlying L1 hypervisors (a) share the memory image of the L2 VM, (b) optionally partition the responsibility for scheduling its virtual CPUs (VCPUs), and (c) optionally partition the responsibility for servicing I/O requests at a device-level granularity. FIG. 2 illustrates this definition for an L2 VM running on two L1 hypervisors (as in V2).

Note that the VCPUs and virtual I/O devices of the L2 VM could be asymmetrically distributed across L1 hypervisors. For example, in FIG. 2, alternatively three VCPUs could be assigned to Hypervisor 1 and one to Hypervisor 2; or even all to the former and none to the latter. Further note that the I/O responsibility may be partitioned among L1 hypervisors only if the VCPUs are partitioned. For example, if Hypervisor 1 handles all the VCPUs of the L2 VM, then Hypervisor 2 is automatically excluded from relaying I/O requests or delivering device interrupts on behalf of the L2 VM.

The present technology enables cloud users to run guest VMs simultaneously on multiple colocated, but isolated, hypervisors. Cloud providers execute the hypervisors, each potentially developed and/or maintained by a different entity, and each exposing one or more hypervisor-level features the cloud user.

The Span technology provides a feasible multi-hypervisor VM, and provides systems support for an L2 VM that simultaneously runs on two L1 KVM hypervisors (as in V2). This two-hypervisor L2 VM (henceforth called Span VM) runs an unmodified guest operating system. All systems support is implemented entirely in the L0 and L1 hypervisors. A Span VM's memory image is shared, and its VCPU state and I/O activity distributed, across two L1s. Using macro and micro benchmarks, a Span VM has been demonstrated to achieve performance comparable to traditional VMs.

Span is not limited to only two L1 hypervisors, and can readily support more than two (V3), and support a hybrid L1-L0 mode (V4).

The benchmarked prototype uses the shadow-on-EPT [7] memory translation mechanism in KVM. However, other EPT translation mechanisms may be supported, for example, a more efficient nested EPT [27] translation mechanism which was recently added to mainline KVM. The use of shadow-on-EPT significantly limits the performance of Span VMs (just as it does for standard nested VMs) due to the large overhead of handling L2 VM Exits.

Span VMs presently run with virtio devices [34], but can be implemented to support direct device assignment and Single Root I/O Virtualization and Sharing (SR-IOV) [8, 9, 30]. The use of virtio negatively impacts the I/O performance of the benchmarked system, and therefore a direct-device assignment to L1 hypervisors would have improved performance.

Finally, both L1 hypervisors presently run KVM. Main Linux/KVM releases do not fully support non-KVM hypervisors as L1 guests [44], although there is some anecdotal evidence of attempts to run legacy Xen as an L1 guest on KVM. Even though both L1 s presently run KVM, each could potentially offer different services to Span VMs, such as an intrusion detection system or a VM introspection system running in one L1 while the other L1 performs standard resource management.

According to the present technology, the multiple hypervisors are provided with distinct levels of privilege or restrictions within the operating environment, distinct from their functionality. In some cases, the VM may execute on various hypervisors that have different respective privileges and/or security models. It is also possible for the VMs to execute on distinct hardware.

The Span technology may also be used in conjunction with other technologies, such as swapping, virtual memory schemes, live migration, and the like.

It is therefore an object to provide a multi-hypervisor VM which can simultaneously run on multiple L1 hypervisors. The latter can co-exist in an ecosystem providing diverse hypervisor-level services.

It is a further object to provide a multi-hypervisor VM that simultaneously uses services from two KVM L1 hypervisors, each offering different services.

It is another object to provide a multi-hypervisor virtual machine, comprising: a unitary host machine; a virtual machine which relies on at least two concurrently available hypervisors to interface with the physical host system; and at least two hypervisors, the virtual machine being configured to concurrently communicate with the at least two hypervisors to execute on the unitary host machine.

It is a further object to provide a method for providing multiple hypervisors for a virtual machine, comprising: providing a unitary host machine; providing at least two hypervisors which are concurrently available and independently execute on the unitary host machine; and executing a virtual machine which relies on the at least two concurrently available hypervisors to interface with the physical host system, the virtual machine having a memory map which has portions accessible by each of the at least two hypervisors.

It is another object to provide a method for providing multiple hypervisors for a virtual machine, comprising: providing a virtual machine supporting execution of a guest operating system and having a memory map, the guest operating system supporting execution of applications, on hardware resources of a unitary host machine; providing at least two concurrently available and independently executing hypervisors which interface the virtual machine to the unitary host machine, the at least two hypervisors each having access to at least a respective portion of the memory map; performing a first action by the virtual machine which employs resources provided by a first hypervisor of the at least two concurrently available and independently executing hypervisors; performing a second action by the virtual machine which employs resources provided by a second hypervisor of the at least two concurrently available and independently executing hypervisors; and servicing at least one input/output request of the virtual machine by the first hypervisor, substantially without interference by the second hypervisor.

According to various aspects, one hypervisor may be hierarchically inferior to another hypervisor. According to another aspect, the at least two hypervisors may be at a common hierarchical level. The hypervisors may have respectively different execution privilege, even if at the same hierarchical level.

The existence of the at least two hypervisors may be transparent to a guest operating system which executes on the virtual machine. An operating system and applications of the virtual machine may execute substantially without explicit control over the selection of respective hypervisor actions.

The at least two hypervisors share a common memory image of the virtual machine. The memory map associated with the virtual machine for each of the at least two hypervisors may be identical. The memory map associated with the virtual machine may be associated exclusively with a single hypervisor. A plurality of hypervisors may partition responsibility for scheduling at least one respective virtual central processing unit.

The at least two hypervisors may each be respectively associated with a different number of virtual central processing units. The at least two hypervisors may offer different services to the virtual machine.

A single hypervisor associated with a virtual central processing unit may be selected for relaying input/output requests from other hypervisors.

A single hypervisor associated with a virtual central processing unit may be is selected for delivering device interrupts to other hypervisors.

A single hypervisor associated with a virtual central processing unit may be selected for delivering device interrupts to the virtual machine on behalf other hypervisors.

A plurality of virtual machines may be provided, wherein a plurality of virtual machines each relies on at least two concurrently available hypervisors to interface with the physical host system. Responsibility for servicing input/output requests of the virtual machine may be partitioned at a device-level granularity among a plurality of hypervisors.

A single hypervisor controlling a virtual central processing unit of the virtual machine may be selected for relaying input/output requests generated from the virtual machine on at least one other virtual central processing unit controlled by another hypervisor.

A single hypervisor may be selected for relaying device interrupts to another hypervisor for delivery to a virtual central processing unit of the virtual machine controlled by the other hypervisor. The device interrupts may be generated by at least one hardware device, and delivered to a respective virtual central processing unit of the virtual machine per an interrupt affinity specified by a guest operating system executing in the virtual machine.

A single hypervisor may be selected for relaying device interrupts on behalf of at least one other hypervisor controlling at least one virtual central processing unit of the virtual machine.

The virtual machine may be configured to execute a guest operating system which supports a polling mode driver for receiving communications from the at least one hypervisor substantially without interrupts.

One hypervisor may have exclusive control over at least a portion of the memory map.

The various hypervisors have respectively different operating privileges.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows Multi-hypervisor Virtual Machines, in which L0 is the level-0 hypervisor. H1, H2, H3, and H4 are level-1 hypervisors that run on L0. V1, V2, V3, and V4 are level-2 VMs. V1 runs on H1. V2 runs on H1 and H2. V3 runs on H2, H3, and H4. V4 runs on H4, and L0.

FIG. 2 shows resource distribution in Multi-hypervisor Virtual Machines; the Memory of the L2 VM is shared across the two L1 hypervisors, whereas its VCPUs and virtual devices may be distributed.

FIG. 3 shows memory translation in non-nested, traditional nested, and multi-hypervisor VM.

FIG. 4 shows an overview of virtio architecture.

FIG. 5 shows virtio operation with Span VMs, in which kicks generated by Span VM at the L1target are redirected to QEMU at L1source.

FIG. 6 shows a graph of one-time setup overhead.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Design and Implementation

An important design requirement is transparency for the Span VM, i.e. the L2 guest kernel and applications should remain unmodified and oblivious to the fact that two L1 hypervisors and the L0 hypervisor coordinate its initialization and runtime management. There are four aspects to consider in the design of Span VMs: (1) Initialization, (2) Memory management, (3) VCPU distribution and scheduling, and (4) I/O processing.

Initialization of Span VMs The goal of initialization step is to have a single Span VM running under the control of two L1 hypervisors. A Span VM is initiated as a regular L2 VM at one of the L1 hypervisors, called the L1source. The second L1 hypervisor, called the L1target, also initiates its own instance of an L2 VM, but maps the L2 memory, VCPU and I/O device states to that initialized by L1source. The two instances of L2 VMs are referred to as sub-VMs of the Span VM. Once the initialization is complete, L1source and L1target work as peers in managing the Span VM.

The three major initialization steps are (a) sharing the Span VM's memory, (b) distributing Span VM's VCPUs, and (c) distributing Span VM's virtual I/O devices, across L1source and L1target.

Memory Initialization and Runtime Management

Consider memory translation in non-nested VMs, i.e. an L1 VM, as shown in FIG. 3(a). As each page is accessed for the first time by the L1 VM during runtime, the corresponding accesses are trapped and physical memory pages are assigned by the L0 hypervisor's memory management subsystem. Subsequent accesses to an already allocated L1 virtual address (VA) are translated using hardware-level page table support. Specifically, an L1 VA is first translated to the L1 guest physical address (GPA) and then to the L0 host physical address (HPA).

For standard nested VMs and Span VMs, as shown in FIG. 3(b), there are two possible configurations for memory translation [7]: (1) shadow-on-EPT and (2) nested EPT (also called multi-dimensional paging in [7]). The nested EPT configuration is more efficient performance-wise [27, 44], and therefore may be preferred for that and other reasons.

3.2.1 Shadow-On-EPT Configuration

FIG. 3(b) shows the memory translation for standard nested VMs using shadow-on-EPT configuration. When a page is allocated for the first time, its page mappings must be updated in both L1 and L0 hypervisors. Specifically, during runtime, an additional shadow page table in the L1 hypervisor translates from the L2 VA to the L1 GPA by compressing the translation (L2 VA)→(L2 GPA)→(L1 GPA). L1 GPA is then translated to L0 HPA using a second-level page table (i.e. EPT for Intel VT-x or NPT for AMD-V).

FIG. 3(c) shows memory translation for Span VMs using shadow-on-EPT configuration. The memory initialization step lays the groundwork to ensure that an L2 VA is translated to the same HPA irrespective of whether the VA is accessed from the sub-VM at L1source or the one at L1target. In other words, an L2 VA must lead to the same HPA irrespective of the translation route, i.e. (L2 VA)→(L1source GPA)→(L0 HPA) or (L2 VA)→(L1target GPA)→(L0 HPA). Since each L2 VA that is accessed via the two sub-VMs leads to the same HPA, any memory write performed by the sub-VM at L1source is immediately visible to the sub-VM at L1target′ and vice versa. Thus the two sub-VMs behave as if they are part of a single Span VM at the L2 level.

L0 needs to know which L1 GPAs are allocated for the L2 sub-VMs by each L1 hypervisor so that L0 can map the corresponding L1 GPAs to same HPAs. When instantiating their respective sub-VMs, both L1 hypervisors set aside requisite number of pages in their GPA space for the Span VM. (These pages do not need to be contiguous, but the benchmarked implementation allocates them in 4 MB chunks.) Both L1s then notify the identity of these reserved GPA pages to the L0 hypervisor via hypercalls. The L0 hypervisor ensures during runtime that the two reserved GPA spaces map to the same HPA space. In other words, if a physical page is allocated for a GPA reserved in L1source′ then the corresponding page for the GPA reserved in L1target is mapped to the same physical page, and vice versa.

While it may appear inefficient at the first sight to reserve L1 GPA space equal to the size of Span VM's memory, note that the reservation is only in the L1 GPA space; no physical memory is allocated for the Span VM until the respective L2 VAs are first written to. Reserving L1 GPA simplifies the implementation by eliminating the chance of exhausting L1 GPA space during runtime

Physical memory allocation for the Span VM occurs when it writes to an L2 VA for the first time during runtime. Since unallocated VAs are backed by anonymous pages, a first-time write to a VA results in a page fault. If the first level translation, i.e. (L2 VA)→(L1 GPA), is missing in the shadow page table, then the L1 hypervisor assigns a page from the reserved GPA space to the faulting L2 VA.

When the L0 hypervisor handles a missing second-level address translation, i.e. (L1 GPA)→(L0 HPA), from L1source′ it first checks whether the faulting L1 GPA belongs to a reserved GPA space for the sub-VM at L1source. If so, and if a physical page was already allocated to L1target for the corresponding L1 GPA, then L0 maps the same physical page to the faulting L1 GPA in L1source. Else a new physical page is allocated to the faulting L1 GPA. Conversely, if L1target faults on a L1 GPA reserved for its L2 sub-VM, then L0 attempts to locate and map the corresponding physical page allocated to L1source. Thus the runtime page allocation is symmetrical whether the initial page access happens from L1source or L1target.

Concurrent page faults: Finally, consider two L2 VCPUs in two different sub-VMs (on different L1s) running on two different physical CPUs, that fault on access to the same L2 page at the same time. In this case, the Span-specific code in the L0 hypervisor serializes any concurrent updates to the EPT translation for both L1s. In other words, if the (L1 GPA)→(L0 HPA) mapping doesn't exist for the faulting page in both L1s, then the L0 hypervisor ensures that the page-fault handlers for both faults map the two faulting L1 GPAs to the same HPA. However, if at least one EPT-level translation exists for the concurrently faulting L2 page, then any other missing translations (namely either of the first-level shadow page table translations or the peer EPT translation) can be processed normally without any coordination between the two L1s.

Nested EPT Configuration

FIG. 3(d) shows the memory translation in standard nested VMs using nested EPT configuration. As with shadow-on-EPT configuration, the first-time that a page needs to be allocated, the page mappings must be updated in both L1 and L0 hypervisors. However, instead of constructing a shadow page table in the L1 hypervisor that translates from the L2 VA to the L1 GPA, the nested EPT configuration constructs a “shadow EPT” in L0 that translates from L2 GPA to L0 HPA. This is achieved by compressing the lower two levels of translation (L2 GPA)→(L1 GPA)→(L0 GPA). Performance-wise, this configuration is more efficient than shadow-on-EPT because the (L2 GPA)→(L1 GPA) mapping changes less frequently than the (L2 VA)→(L2 GPA) mapping. Hence fewer VM Exits, world switches, and redirections through the L0 hypervisor are needed to maintain the shadow EPT. FIG. 3(e) shows a memory translation mechanism in Span VMs that will use nested EPT configuration. During initialization, as before, each L1 hypervisor will reserve L1 GPA space for the L2 sub-VMs, but no physical memory will be allocated. During runtime, page faults are handled as follows. If the first level translation, i.e. (L2 VA)→(L2 GPA), is missing then let the L2 guest assign an L2 GPA page. If the second level translation (L2 GPA)→(L0 GPA) is missing in the shadow EPT constructed via (say) L1source′ then L0 first lets L1source to populate the internal mapping (L2 GPA)→(L1 GPA) by using a page from its reserved L1 GPA space for the corresponding L2 sub-VM. Next, if a physical page was already allocated to L1target for the corresponding L1 GPA, then L0 will map the same physical to L1source′ else a new physical page will be allocated. Conversely, if the fault relates to a missing shadow EPT entry via L1target then L0 will try to locate and map the corresponding physical page allocated to L1source. Regardless, the two shadow EPTs constructed via either L1 will finally translate a given L2 GPA to the same HPA. However, the two shadow EPTs won't necessarily be identical at any instant since each L2 sub-VM may access a different subset of L2 GPA space, populating different shadow-EPT entries.

Concurrent page faults will be handled as in the case of shadow-on-EPT configuration; L0 will serialize any concurrent attempts via different sub-VMs (on different L1s) to update the shadow EPT entries for the same L2 GPA.

VCPU Distribution and Scheduling

“VCPU distribution” for a Span VM refers to the fact that the virtual CPU (or VCPU) is a logical representation of a physical CPU (PCPU) and is exported from a hypervisor to a VM. Informally, this logical representation consists of a program counter and its associated execution context (registers, stack pointer, etc). The number of VCPUs seen by a VM could be more, equal, or less than the number of PCPUs in the machine. A hypervisor manages VCPUs in two ways: through spatial scheduling (VCPU-to-PCPU assignment) and through temporal scheduling (when and how long does a VCPU remain mapped to a PCPU).

A Span VM can “run” on two (or more) L1 hypervisors simultaneously, that is, the responsibility for temporal and spatial scheduling of Span VM's VCPUs is distributed among the two underlying hypervisors. The L2 VCPU may be controlled entirely (i.e. both spatially and temporally) by one L1 hypervisor during the lifetime of the Span VM.

The initialization step determines which L2 VCPU of the Span VM is controlled by which L1 hypervisor. The distribution of VCPUs could be equal, where each L1 hypervisor controls the same number of VCPUs, or it could be unequal, where different L1 hypervisors may control different number of VCPUs. For example, if the Span VM is configured to have 4 VCPUs, then after the initialization step, 2 VCPUs could execute on L1source and 2 VCPUs could execute on L1target. Alternatively, the VCPU distribution could also be 3 and 1, 1 and 3, 4 and 0, or 0 and 4. The last two distributions would imply pure memory mapping and no VCPU control at one of the L1s.

A preferred approach for distributing the VCPUs of the Span VMs is as follows. The L1source begins by initiating its L2 sub-VM, initializes the memory state as described above, and initializes all the VCPUs of the Span VMs as it would for regular nested VMs. Once the guest OS in the L2 sub-VM boots up, L1source hands over the control of scheduling a subset of the L2 VCPUs to L1target. Thus L1target does not initialize any VCPUs from scratch its L2 sub-VM; rather it accepts a preinitialized subset of VCPUs from L1source. For example, if the Span VM is configured with two VCPUs, then after the VCPU distribution step, one VCPU will be active on L1source and the second will be active on L1target. The transfer of VCPU state is achieved by using a variant of the VM migration logic, wherein only the VCPU and device states are transferred, but memory transfer is skipped (since L2 memory is already shared across L1source and L1target).

Implementation-wise, QEMU represents VCPUs as user space threads. Hence, to split the responsibility of executing L2 VCPUs across different L1 hypervisors, the execution of complementary set of threads in the corresponding L1 QEMU processes may be paused. During initialization, the VCPU state is transferred from L1source to L1target by modifying the existing pre-copy QEMU migration code. After VCPU state is transferred, complementary set of the QEMU VCPU threads are paused on either side.

The guest OS in the Span VM will try to schedule its work (threads/processes/interrupt handlers) on all of the VCPUs that it sees, subject to affinity rules configured by the administrator (such as process affinity or IRQ affinity). A process/thread within a guest OS can be generally migrated from one VCPU to another, except in cases when some of them may be pinned to certain VCPUs. Similarly, an interrupt handler can execute on any VCPU allowed by IRQ affinity configuration.

One of the issues in the Span VM design is about what happens when the L2 guest OS tries to migrate a process from one L2 VCPU running on, say, L1source to another L2 VCPU running on L1target. Keep in mind that the socalled “migration” of a process from one VCPU to another basically boils down to moving the process task structure (task struct in Linux) from the ready queue of one VCPU to that of another. So moving a process across VCPUs should just be an update operation on kernel data structures that are kept in the L2 guest's main memory. Ideally, the existing scheduling mechanisms in the guest OS for changing VCPU assignment for processes should work inside a Span VM as well. However, there are subtle architecture-level issues such as flushing stale TLB entries for the migrating process from the old VCPU, which requires an inter-processor interrupt (IPI) from the new VCPU to the old VCPU. In the above example, these IPIs and any similar notifications would need to be forwarded from one L1 to another when an L2 process is migrated across sub-VM boundaries.

Consider what happens when concurrently executing VCPUs on different hypervisors attempt to access (read/write) common memory locations (such as kernel data structures). The Span VM's memory image typically resides in the DRAM of a single machine. So it is acceptable if two different VCPUs controlled by two different hypervisors access common memory locations. All existing locking mechanisms in the L2 guest would work correctly because the locks themselves are stored in the L2 main memory. Thus memory consistency is not compromised by distributing L2 VCPUs over multiple L1s because the L2 main memory is shared by L1source and L1target.

I/O Processing in Span VMs

The I/O subsystem for a VM can be configured in one of three modes: device emulation [41], para-virtual devices [34, 3], or direct assignment [8, 9, 30]. Paravirtual devices perform better than device emulation. Direct assignment, including SR-IOV [30], yields the best performance, since it allows a VM to bypass intermediate software layers while interacting with the I/O device.

I/O processing in Span VMs needs to account for the fact that a single L2 VM is now associated with two L1 hypervisors. Three design options are (a) to allow both hypervisors to manage all of L2's I/O devices, or (b) to delegate the control of each I/O device belonging to L2 to one of the two L1 hypervisors, or (c) to allow the L2 VM to directly control its I/O devices. Option (a) is very complicated to implement due to the need to manage concurrency and device control while providing little functional benefits. Option (b) is simpler, but requires coordination among the two L1s. Option (c) is the simplest, provided that hardware and hypervisor-level support is available.

A prototype was implemented using Option (b), delegating the I/O control to a single L1 hypervisor using para-virtual virtio drivers [34].

Virtio Overview

FIG. 4 shows the high level overview of standard virtio architecture. The guest OS in the VM runs paravirtual frontend drivers, such as for virtual block and network devices. The QEMU process hosts the corresponding virtio backends. The frontend and the backend exchange I/O requests and responses via a vring, which is basically a shared buffer. When an I/O request is placed in the vring, the frontend notifies QEMU through a kick operation, i.e. is a trap leading to VM Exit. The kick is redirected to QEMU via the KVM kernel module. The QEMU process retrieves the I/O request from the vring, issues the request to the native drivers as an asynchronous I/O. Once the I/O operation completes, QEMU injects an I/O completion interrupt to the guest OS. When the VM resumes, the I/O completion interrupt is delivered to a VCPU according to the IRQ affinity rules in the guest OS. The interrupt handler in the guest invokes the frontend driver, which picks up the I/O response from the vring.

The Multiple Backend Problem

The first problem relates to the fact that, since a Span VM runs on two L1 hypervisors, it is associated with two QEMU processes, one on L1source and another on L1target′ as shown in FIG. 5. Thus a single virtio frontend with one vring is now associated with two virtio backends. If both virtio backends access the vring concurrently, race conditions would result in corruption of the vring buffers. To solve this problem, only one virtio backend is allowed to pick up I/O requests and deliver I/O responses through the vring. So, for example, assume that the virtio backend at the L1source is configured to interact with the vring. If an L2 VCPU running at L1source issues an I/O request, then the corresponding kick will be handled by L1source QEMU. However, if an L2 VCPU running at the L1target issues an I/O request, then the corresponding kick will be redirected to the QEMU at L1target. The backend in L1target QEMU will not access the vring to fetch the I/O request. Instead, the QEMU backend at L1target is modified so that it redirects the kick one more time to the QEMU at L1source. At this point, the QEMU backend at the L1source fetches the I/O request from the vring and processes the request via asynchronous I/O. Once the I/O completes, the L1source QEMU injects an I/O completion interrupt into the guest to notify the frontend.

The benchmarked prototype uses two virtio serial devices exported from the L0 to each L1 in order to redirect the virtio kick information across L1s; this mechanism could be replaced by a more efficient channel, such as shared memory. Also note that presently it is unnecessary to synchronize the L2's IOAPIC state across L1 boundaries because only one L1 is designated to handle each I/O device for L2, and frontend kicks are forwarded to the designated L1. Thus, it is acceptable even if the L2's IOAPIC state maintained within the two L1s are not synchronized because only the designated L1's IOAPIC state is relevant for an L2 I/O device.

Lost Interrupt

The second problem relates to the fact that each L1 suppresses complementary set of VCPUs for L2 for VCPU distribution and this could interact negatively with I/O interrupt processing. For simplicity, assume that L1 has two VCPUs—L1source runs VCPU0 and pauses VCPU1 whereas L1target runs VCPU1 and pauses VCPU0. Assume that IRQ affinity rules in the L2 guest permit I/O interrupt delivery to both VCPU0 and VCPU1. Let's say an I/O operation completes on L1source. KVM in L1source would follow the affinity rules and inject the I/O completion interrupt to VCPU1. Since VCPU1 is suppressed on L1source′ the interrupt would never be processed by L2 guest, and the I/O would never complete.

To solve this problem, the IOAPIC code in both L1 KVMs is modified to deliver interrupts only to L2 VCPUs that are not suppressed (active) in the corresponding L1. While this may temporarily override IRQ affinity settings in the L2 guest, it prevents the problem of lost interrupts. If any L2 guest requires the IRQ affinity settings to be honored for correct operations, then an alternative is to redirect L2 interrupts from one L1 to another when needed. This is optional, and not implemented in the benchmarked prototype.

Network Receive

The above described solutions works as is for read and write requests on virtio-blk device and packet send requests on virtio-net device. For packet receive operations on virtio-net device, an additional complication arises. The Span VM has only one network identity (IP address, MAC address). Assume that a bridged mode network configuration is employed, where a software bridge in L0 determines where each incoming packet should be delivered. For Span VM, incoming packets could be delivered through either L1source or L1target. Which path the L0 software bridge chooses depends upon the reverse learning algorithm. If outgoing packets from Span VM consistently exit through L1source then incoming packets will be delivered through L1source as well. Likewise for L1target. However, if outgoing packets switch back and forth between L1source and L1target as exit paths, then the L0 software bridge may simply broadcast the incoming packets for Span VM to both paths, which would lead to duplicate packet deliveries to Span VM. To avoid this problem, the outgoing packets from Span VM may be forcibly restricted to exit via only a designated L1 (say L1source for the sake of discussion) and not the other. As a result, the reverse learning L0 software bridge would deliver any incoming packets for L2 (and the corresponding RX interrupts) only to L1source from where all outgoing packets exit. L1source in turn injects the RX interrupt to one of the active VCPUs of the L2 sub-VM that it hosts; it does not need to forward the RX interrupt to L1target even if the destination L2 VCPU for the packet is running on L1target.

Polling Driver Alternative

To avoid the multiple backend and lost interrupt problems described above for block devices, an alternative solution was implemented in which virtio-blk device was converted into a polling mode driver. Once the virtio block device is created, a QEMU thread is created to check the available ring whether there are requests from the L2 guest. If there are available requests, the QEMU backend pops the requests from available ring, and submits the requests to I/O threads in QEMU. The callback functions do not inject interrupts into the L2 guest. On L2 guest side, once a virtio block device is detected, the front-end driver creates a kernel thread to keep checking whether there are finished requests in the used ring from the backend. If so, the kernel thread will wake up the waiting process. While this approach avoided the above two problems, the CPU overhead of the polling mode driver was too high, on top of shadow-on-EPT nested virtualization overheads.

Inter-Processor Interrupts

In addition to redirecting I/O kicks from QEMU, any inter-processor interrupts (IPIs) that are issued from a VCPU on one L1 are redirected to a VCPU on another, such as to deliver a TLB flush when migrating L2 processes across L2 VCPUs. In standard nested VMs, IPIs between VCPUs are intercepted and delivered by the KVM module. If the sender and the target VCPUs of the IPI are on the same physical CPU, then when the next time the target VCPU switches to guest mode, the injected IPI will be triggered. If the target VCPU is in guest mode, a reschedule IPI message will be sent to the target physical CPU where the VCPU is running in guest mode, which will cause a VM exit, when the next time the target VCPU enters guest mode, the injected IPI will be found by the guest.

For Span VMs, cross-L1 IPIs are transferred. For example, if an IPI from an L2 VCPU running on L1source is meant for an L2 VCPU running on L1target then KVM at L1source transfers the IPI information to the KVM at L1target′ which then injects the IPI into the target L2 VCPU. Again, the benchmarked prototype uses serial virtio devices exported from L0 to L1 to transfer IPIs across L1s; this mechanism could be replaced by a more efficient channel, such as shared memory.

Evaluation

The evaluation compares macro benchmark performance of Span VMs against standard nested VMs and measures low-level sources of overheads in Span using micro benchmarks.

The evaluation setup consists of a dual quad-core Intel Xeon 2.27 GHz server with 70 GB memory running Ubuntu 9.04 with Linux kernel version 2.6.32.10. The hypervisor running on the host is qemu-kvm-1.0 and kvmkmod-3.2. For both L1source and L1target guests, an Ubuntu 10.04 guest with kernel version 3.2.2 was used. Each L1 is configured with 4 GB memory and two VCPUs. The hypervisors running on both L1 guests are qemu-kvm-1.2 and kvm-kmod-3.2. Ubuntu 10.04 with kernel version 2.6.28-generic was used for the nested and Span VMs, both of which are configured with two VCPUS and 2 GB memory.

Macro Benchmarks

The performance of two CPUintensive benchmarks, namely Kernbench [24] and SPECjbb2005 [39] were measured Kernbench measures the time taken when repeatedly compiling the Linux kernel. Kernbench is primarily a CPU and memory intensive benchmark but also performs I/O to read and write files. Kernbench was tested with the default configuration options and averaged over the results over three runs. SPECjbb2005 measures the server-side performance of Java runtime environments. The benchmark emulates a 3-tier system, which is the most common type of server-side Java application. SPECjbb2005 measures business operations per seconds (bops) by averaging the total transaction rate in a run from the expected peak number of warehouses. SPECjbb2005 is primarily a CPU-intensive benchmark. Kernbench and SPECjbb2005 were run in four different settings. For accurate comparison, each setting ran the benchmarks with the same number of CPUs and memory.

1. Host with two physical CPUs.

2. L1 guest with two VCPUs running on an L0 with eight physical CPUs.

3. L2 guest with two VCPUs running on L1 with two VCPUs running on L0 with eight physical CPUs.

4. L2 Span guest with two VCPUs running on two L1s which each having two VCPUs and running on a L0 with eight physical CPUs.

Table 1 for Kernbench shows that Span VM incurs 6.3% overhead compared to the traditional nested VM, 361.2% overhead compared to L1, and 395.6% overhead compared to host. Table 2 for SPECjbb2005 shows that Span VM has 1.3% performance degradation compared to the standard nested VM, 6.4% performance degradation compared to L1, 23.8% compared to host. Thus Span VM performs comparably against standard nested VMs for both Kernbench and SPECjbb2005. Most of the overheads is due to the redirected interrupts and virtio kicks across L1s. The overhead of IPI redirection, I/O interrupt redirection and page fault servicing are analyzed. Also note that the performance numbers for standard nested VM are worse than the numbers reported in the Turtles project [7], mainly because the Span VM uses a shadow-on-EPT configuration rather than multidimensional paging (nested EPT) as used in Turtles.

TABLE 1 Comparison of Kernbench performance. Kernbench Host Guest Nested Span Run time 136.15 146.31 634.70 674.79 STD dev. 8.09 1.13 8.79 9.68 % overhead — 7.5 366.2 395.6 vs. host % overhead — — 333.8 361.2 vs. guest % overhead — — — 6.3 vs. nested % CPU 97 90 100 100

TABLE 2 Comparison of SPECjbb2005 performance. SPECjbb2005 Host Guest Nested Span Score 35416 28846 27289 27000 STD dev. 1058 1213 1863 1898 % degradation — 18.6 22.9 23.8 vs. host % degradation — — 5.4 6.4 vs. guest % degradation — — — 1.3 vs. nested % CPU 100 100 100 100

For I/O-intensive workloads, dd and netperf were used to measure the I/O throughput using virtio block and network devices. The command dd in Linux copies data of specified size between two devices. Netperf [28] is a network throughput measurement benchmark between a client and a server. As can be seen from Tables 3 and 4, a Span VM delivers similar throughput with dd and netperf as a standard nested VM does. For dd, Span VM has 6.6% degradation and for netperf, it has 9.5% degradation compared to the traditional nested VM. Both standard nested VM and Span VMs have significantly lower throughput than a non-nested VM and native execution. The reason is that I/O operations using virtio generate numerous virtio kicks, which are basically notifications from virtio front-end in the L2 guest to the virtio back-end in QEMU; these notifications are implemented using VM Exits via the L1 KVM kernel module. Processing each L2 VM Exit requires multiple L1 VM exits, leading to heavy CPU load.

TABLE 3 Comparison of dd throughput. dd Host Guest Nested Span Throughput (MB/s) STD dev. 80.1 65.15 21.3 19.89 5.05 1.98 2.33 1.67 % overhead vs. host 18.7 73.4 75.2 % overhead vs. Guest 67.3 69.5 % overhead vs. nested 6.6

TABLE 4 Netperf performance with 16 KB message size. netperf Host Guest Nested Span Throughput (Mbps) STD dev. 940.5 930.17 343.92 311.36 0.38 0.64 26.12 12.82 % overhead vs. host 1.1 63.4 66.9 % overhead vs. Guest 63.3 66.5 % overhead vs. nested 9.5

Micro Benchmarks

Span VM was tested with micro-benchmark to evaluate low-level system overheads.

One-Time Setup Overhead

After a standard L2 VM is booted up on L1source′ initializing it into a Span VM involves three major steps: (1) sharing the Span VM's memory, (2) distributing its VCPUs, and (3) distributing virtual I/O devices across the two L1s. Sharing the Span VM's memory involves pre-allocating guest physical addresses in L1, and invoking hypercalls to convey these addresses to L0. The benchmarked prototype implements these setup operations as a variant of the VM migration logic in the user-space QEMU process in L1 and the kernel-space KVM in the L1 and L0 hypervisors. FIG. 6 shows the breakup of this one-time setup overhead as the L2 memory size is increased. Most of the setup overhead comes from invoking hypercalls to convey the pre-allocated L1 guest physical addresses to L0. This cost increases as the size of the Span VM increases since more hypercalls are invoked. This overhead could potentially be reduced through more efficient batching of addresses conveyed to L0 through hypercalls. The costs of distributing VCPU and device I/O states is much smaller in comparison. The total time to set up a 2 GB Span VM is around 135 ms.

Page-Fault Servicing Overhead

Handling page-faults in Span VMs requires additional work in L0 hypervisor. Specifically, the EPT fault handler needs to ensure that an L2 VM's faulting virtual address maps to the same physical address, irrespective of whether it is accessed through L1source or L1target.

Table 5 compares the average page-fault servicing times for traditional nested and Span VMs. This time includes the additional work required to retrieve a physical page mapping from a table in L0, if the faulting address has been already allocated, otherwise the time required to allocate a new page, plus the time to map the faulting L1 GPA to the newly allocated L0 physical page. As seen from the table, Span introduces an average of 1.01 μs overhead in L1 shadow page-fault servicing time and 7.36 μs overhead in L0 EPT page fault servicing time.

TABLE 5 Average page fault service time. Nested Span Difference L1 shadow and interrupt delivery (μs) 6.07 7.08 1.01 Lo EPT page fault (μs) 6.71 14.07 7.36

Redirection of IPI & Virtio Kicks

Table 6 shows that Span introduces an overhead of around 1.6 ms in redirecting an IPI between two VCPUs on different L1s over traditional IPI delivery between two colocated VCPUs in a standard nested VM. The overhead arises from sending the IPI messages from one one L1 to another using a virtio serial device-based communication channel between the two L1s.

TABLE 6 IPI redirection overhead. Nested Span Difference IPI delivery overhead (μs) 18 1672 1654

The overhead of redirecting virtio kicks across L2s was tested by exchanging kick message repeatedly between the two QEMUs using the virtio serial port based communication mechanism. The kick redirection mechanism was found to take 916 μs longer than kick delivery in standard nested VMs, as shown in Table 7. The virtio serial port based redirection mechanism can be replaced by a more efficient channel, such as inter-L1 shared memory. Also, the use of direct device assignment at L2 will obviate the need of redirecting the virtio kicks.

TABLE 7 Virtio kicks redirection overhead. Nested Span Difference Virtio kicks overhead (μs) 116 1032 916

CONCLUSION

Multi-hypervisor VMs, unlike standard nested VMs, execute simultaneously on multiple L1 hypervisors. Span provides systems support for an L2 VM that simultaneously runs on two L1 KVM hypervisors. Span works by sharing the L2 VM's memory footprint across the two L1 hypervisors and by distributing the responsibility of scheduling L2's VCPUs and I/O among the two L1s. The measured performance of Span VMs using various micro and macrobenchmarks is comparable to standard nested VMs.

The I/O performance of Span VMs may be improved through the use of direct device assignment and SR-IOV. Span VMs could run on more than two L1 hypervisors, mixed mode L1-L0 hypervisors, and a mix of commodity L1 hypervisors such as Xen and KVM. The Span VMs may also be subject to live migration.

Span VMs enable capabilities beyond traditional VM-Hypervisor systems by allowing an L2 VM to pick and choose among multiple L1 services, instead of solely relying on one L1 hypervisor for all services. Span VMs may also provide hypervisor fault-tolerance, wherein a backup L1 can take over an L2 VM's execution in case the primary L1 fails.

While Span typically resides on a single physical machine running one L0 hypervisor, by, for example, extending distributed virtual memory technology and live migration technology, Span can employ a distributed or multiple L0 platform. Therefore, a single physical machine is not a limitation of the technology. However, embodiments of the technology typically employ a single physical machine running one L0 hypervisor.

The invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are, therefore, intended to be embraced therein.

The term “comprising”, as used herein, shall be interpreted as including, but not limited to inclusion of other elements not inconsistent with the structures and/or functions of the other elements recited.

REFERENCES

The following references are expressly incorporated herein by reference in their entirety:

-   [1] AMD Virtualization (AMD-V).     www.amd.com/us/solutions/servers/virtualization. -   [2] Andrew S. Tanenbaum, M. Frans Kaashoek, Robbert van Renesse and     Henri E. Bal. The Amoeba Distributed Operating System-A Status     Report. In Computer Communications, volume 14, pages 324-335, 1991. -   [3] P. Barham, B. Dragovic, K. Fraser, S. Hand, T. Harris, A. Ho, R.     Neugebauer, I. Pratt, and A. Warfield. Xen and the art of     virtualization. SIGOPS Operating Systems Review, 37(5):164-177,     2003. -   [4] M. Beham, M. Vlad, and H. Reiser. Intrusion detection and     honeypots in nested virtualization environments. In Proc. of     Dependable Systems and Networks (DSN), June 2013. -   [5] G. Belpaire and N.-T. Hsu. Formal properties of recursive     virtual machine architectures. SIGOPS Oper. Syst. Rev., 9(5):89-96,     November 1975. -   [6] G. Belpaire and N.-T. Hsu. Hardware architecture for recursive     virtual machines. In Annual ACM Conference, pages 14-18, 1975. -   [7] M. Ben-Yehuda, M. D. Day, Z. Dubitzky, M. Factor, N. Har'El, A.     Gordon, A. Liguori, O. Wasserman, and B.-A. Yassour. The turtles     project: Design and implementation of nested virtualization. In     Proc. of Operating Systems Design and Implementation, 2010. -   [8] M. Ben-Yehuda, J. Mason, J. Xenidis, O. Krieger, L. van Doom, J.     Nakajima, A. Mallick, and E. Wahlig. Utilizing iommus for     virtualization in linux and xen. In OLS '06: The 2006 Ottawa Linux     Symposium, July 2006. -   [9] M. Ben-Yehuda, J. Xenidis, M. Ostrowski, K. Rister, A. Bruemmer,     and L. van Doom. The price of safety: Evaluating iommu performance.     In OLS '07: The 2007 Ottawa Linux Symposium, pages 9-20, July 2007. -   [10] S. Butt, H. A. Lagar-Cavilla, A. Srivastava, and V. Ganapathy.     Self-service cloud computing. In Proceedings of the 2012 ACM     Conference on Computer and Communications Security, CCS '12, pages     253-264, 2012. -   [11] C. Clark, K. Fraser, S. Hand, J. Hansen, E. Jul, C. Limpach, I.     Pratt, and A. Warfield. Live migration of virtual machines. In Proc.     of Network System Design and Implementation, 2005. -   [12] P. Colp, M. Nanavati, J. Zhu, W. Aiello, G. Coker, T.     Deegan, P. Loscocco, and A. Warfield. Breaking up is hard to do:     Security and functionality in a commodity hypervisor. In Proc. of     SOSP, pages 189-202, 2011. -   [13] B. Ford, M. Hibler, J. Lepreau, P. Tullmann, G. Back, and S.     Clawson. Microkernels meet recursive virtual machines. SIGOPS     Operating Sys. Rev., 30(SI):137-151, October 1996. -   [14] T. Garfinkel and M. Rosenblum. A virtual machine introspection     based architecture for intrusion detection. In Network & Distributed     Systems Security Symposium, 2003. -   [15] C. Gebhardt and C. Dalton. Lala: A late launch application. In     Workshop on Scalable Trusted Computing, Chicago, Ill., USA, pages     1-8, 2009. -   [16] R. P. Goldberg. Architecture of virtual machines. In     Proceedings of the Workshop on Virtual Computer Systems, Cambridge,     Mass., USA, pages 74-112, 1973. -   [17] R. P. Goldberg. Survey of virtual machine research. Computer,     7(9):34-45, September 1974. -   [18] A. Graf and J. Roedel. Nesting the virtualized world. In Linux     Plumbers Conference, September 2009. -   [19] M. Hines, U. Deshpande, and K. Gopalan. Postcopy live migration     of virtual machines. In SIGOPS Operating Systems Review, July 2009. -   [20] J.-C. Huang, M. Monchiero, Y. Turner, and H.-H. S. Lee. Ally:     Os-transparent packet inspection using sequestered cores. In     Symposium on Architectures for Networking and Communications     Systems, 2011. -   [21] X. Jiang, X. Wang, and D. Xu. Stealthy malware detection and     monitoring through VMM-based “outof-the-box” semantic view     reconstruction. ACM Trans. Information Systems Security, 13(2):1-28,     March 2010. -   [22] John K. Ousterhout, Andrew R. Cherenson, Frederick Douglis,     Michael N. Nelson and Brent B. Welch. The Sprite network operating     system. In IEEE Computer, volume 21, pages 23-36, February 1988. -   [23] A. Kivity, Y. Kamay, D. Laor, U. Lublin, and A. Liguori. KVM:     the linux virtual machine monitor. In Proc. of Linux Symposium, June     2007. -   [24] C. Kolivas. Kernbench:     ck.kolivas.org/apps/kernbench/kernbench-0.50/. -   [25] D. G. Murray, G. Milos, and S. Hand. Improving xen security     through disaggregation. In Proc. of Virtual Execution Environments,     pages 151-160, 2008. -   [26] J. Nakajima. Enabling Optimized Interrupt/APIC Virtualization     in KVM. In KVM Forum, Barcelona, Spain, November 2012. -   [27] G. Natapov. Nested EPT to make nested VMX faster. In KVM Forum,     Edinburgh, October 2013. [28] Netperf. www.netperf.org/netperf/. -   [29] D. L. Osisek, K. M. Jackson, and P. H. Gum. Esa/390     interpretive-execution architecture, foundation for vm/esa. IBM     Systems Journal, 30(1):34-51, February 1991. -   [30] PCI SIG. Single Root I/O Virtualization and Sharing 1.1     specification -   [31] Phoenix Hyperspace. www.hyperspace.com/. -   [32] G. J. Popek and R. P. Goldberg. Formal requirements for     virtualizable third generation architectures. Communications of ACM,     17(7):412-421, July 1974. -   [33] R. Riley, X. Jiang, and D. Xu. Guest-transparent prevention of     kernel rootkits with vmm-based memory shadowing. In Proceedings of     the 11th International Symposium on Recent Advances in Intrusion     Detection, pages 1-20, 2008. -   [34] R. Russell. Virtio: Towards a de-facto standard for virtual i/o     devices. SIGOPS Oper. Syst. Rev., 42(5):95-103, July 2008. -   [35] J. Rutkowska. Subverting vista kernel for fun and profit. In     Blackhat, August 2006.     www.scalemp.com/mediahub/resources/white-papers. -   [36] Sape J. Mullende, Guido van Rossum, Andrew S. Tanenbaum,     Robbert van Renessey and Hans van Staveren. Amoeba: a distributed     operating system for the 1990s. In IEEE Computer, volume 23, pages     44-53, May 1990. -   [37] A. Seshadri, M. Luk, N. Qu, and A. Perrig. Secvisor: a tiny     hypervisor to provide lifetime kernel code integrity for commodity     oses. In ACM SIGOPS Operating Systems Review, volume 41(6), pages     335-350, 2007. -   [38] E. G. Sirer, R. Grimm, A. J. Gregory, and B. N. Bershad. Design     and implementation of a distributed virtual machine for networked     computers. SIGOPS Oper. Syst. Rev., 33(5):202-216, December 1999. -   [39] SPECjbb2005. www.spec.org/jbb2005/. -   [40] U. Steinberg and B. Kauer. Nova: A microhypervisor-based secure     virtualization architecture. In Proc. of EuroSys, pages 209-222,     2010. -   [41] J. Sugerman, G. Venkitachalam, and B.-H. Lim. Virtualizing I/O     devices on vmware workstation's hosted virtual machine monitor. In     Proceedings of USENIX Annual Technical Conference, 2002. -   [42] R. Uhlig, G. Neiger, D. Rodgers, A. Santoni, F. Martins, A.     Anderson, S. Bennett, A. Kagi, F. Leung, and L. Smith. Intel     virtualization technology. Computer, 38(5):48-56, 2005.     www.pcisig.com/specifications/iov/single root/. -   [43] Vsmp Foundation Architecture. -   [44] O. Wasserman. Nested virtualization: shadow turtles. In KVM     Forum, Edinburgh, Spain, October 2013. -   [45] D. Williams, H. Jamjoom, and H. Weatherspoon. The Xen-Blanket:     Virtualize once, run everywhere. In EuroSys, Bern, Switzerland,     2012. -   [46] L. Xia, K. Hale, and P. Dinda. ConCORD: Easily exploiting     memory content redundancy through the content-aware service command.     In Proc. of High Performance Distributed Computing, -   [47] A. Baumann, P. Barham, P.-E. Dagand, T. Harris, R. Isaacs, S.     Peter, T. Roscoe, A. Schupbach, and A. Singhania. The Multikernel: A     new OS architecture for scalable multicore systems. In Proceedings     of SOSP, Big Sky, Mont., USA, 2009. -   [48] M. Ben-Yehuda, M. D. Day, Z. Dubitzky, M. Factor, N. Har'El, A.     Gordon, A. Liguori, O. Wasserman, and B.-A. Yassour. The Turtles     project: Design and implementation of nested virtualization. In     Proc. of Operating Systems Design and Implementation, 2010. -   [49] M. Chapman and G. Heiser. vNUMA: A virtual shared-memory     multiprocessor. In Proc. of USENIX Annual Technical Conference,     2009. -   [50] H. Chen, R. Chen, F. Zhang, B. Zang, and P. Yew. Live updating     operating systems using virtualization. In Proc. of ACM VEE, Ottawa,     Canada, June 2006. -   [51] B. Cully, G. Lefebvre, D. Meyer, M. Feeley, N. Hutchinson,     and A. Warfield. Remus: High availability via asynchronous virtual     machine replication. In Proc. of Networked Systems Design and     Implementation, 2008. -   [52] A. Dinaburg, P. Royal, M. Sharif, and W. Lee. Ether: malware     analysis via hardware virtualization extensions. In 15th ACM     conference on Computer and communications security (CCS), pages     51-62, 2008. ISBN 978-1-59593-810-7. -   [53] G. W. Dunlap, S. T. King, S. Cinar, M. A. Basrai, and P. M.     Chen. ReVirt: Enabling intrusion analysis through virtualmachine     logging and replay. In Proc. of USENIX OSDI, Boston, Mass., December     2002. -   [54] IPSECS. Kernel Beast rootkit/kbeast-v 1/. -   [55] K. Kourai and S. Chiba. Hyperspector: Virtual distributed     monitoring environments for secure intrusion detection. In     ACM/USENIX International Conference on Virtual Execution     Environments, pages 197-207, 2005. -   [56] B. D. Payne, M. Carbone, M. Sharif, and W. Lee. Lares: An     architecture for secure active monitoring using virtualization. In     IEEE Symposium on Security and Privacy, pages 233-247, 2008. -   [57] J. Toldinas, D. Rudzika, Y. Stuikys, and G. Ziberkas. Rootkit     detection experiment within a virtual environment. Electronics and     Electrical Engineering-Kaunas: Technologija, (8):104, 2009. -   [58] A. Vasilevsky, D. Lively, and S. Ofsthun. Linux Virtualization     on Virtual Iron V Fe. In Proc. of Linux Symposium, pages 235-250,     2005. -   [59] A. Whitaker, R. Cox, and M. Shaw. Constructing services with in     terposable virtual hardware. In Proc. of NSDI, San Francisco.     Calif., 2004. -   [60] Sirer, E. G., Grimm, R., Gregory, A. J., and Bershad, B. N.     Design and implementation of a distributed virtual machine for     networked computers. SIGOPS Oper. Syst. Rev. 33, 5 (December 1999),     202-216. -   [61] Williams, D., Jamjoom, H., and Weatherspoon, H. The     Xen-Blanket: Virtualize once, run everywhere. In EuroSys, Bern,     Switzerland (2012). -   [62] Jian Wang, Kwame-Lante Wright and Kartik Gopalan. XenLoop: A     Transparent High Performance Inter-VM Network Loopback. In     Proceedings of the 17th International Symposium on pages 109-118,     2008. -   [63] A. Cameron Macdonell “Shared-memory optimizations for virtual     machines” Ph.D. Dissertation, University of Alberta 2011, Paul Lu     thesis advisor 

What is claimed is:
 1. A computer system, comprising: at least one hardware processor; a plurality of nested hypervisors, configured to concurrently execute under a privileged hypervisor on the at least one hardware processor, each of the plurality of nested hypervisors having a respective set of execution privileges on the at least one hardware processor lower than a set of execution privileges of the privileged hypervisor on the at least one hardware processor; and a virtual machine configured to concurrently directly communicate with each of the plurality of nested hypervisors, and having a memory map comprising at least one logical memory page of a memory image mapped to a common host physical memory page address, translated through each of the plurality of nested hypervisors, the virtual machine being is configured to: execute a first process directly through a first hypervisor having a first set of execution privileges, wherein the first hypervisor translates the common host physical memory page address of the at least one logical memory page to the logical memory page address, and execute a second process directly through a second hypervisor having a second set of execution privileges, wherein the second hypervisor translates the common host physical memory page address of the at least one logical memory page to the logical memory page address.
 2. The computer system according to claim 1, wherein each of the plurality of nested hypervisors provides at least one service or resource to the virtual machine which is different from another of the plurality of nested hypervisors.
 3. The computer system according to claim 1, wherein at least two of the plurality of nested hypervisors have respectively different sets of execution privileges.
 4. The computer system according to claim 1, wherein a single one of the plurality of nested hypervisors has exclusive control over at least one portion of the respective memory image associated with the virtual machine.
 5. The computer system according to claim 1, wherein at least one respective virtual central processing unit of the virtual machine executes under each of the plurality of nested hypervisors, and the plurality of nested hypervisors partition responsibility for scheduling of the at least one respective virtual central processing unit.
 6. The computer system according to claim 1, wherein input- output scheduling for a first of the plurality of nested hypervisors is handled by a second of the plurality of nested hypervisors.
 7. The computer system according to claim 1, further comprising a second virtual machine, the virtual machine and the second virtual machine each relying on at least two concurrently available ones of the plurality of nested hypervisors to interface with the privileged hypervisor.
 8. The computer system according to claim 1, wherein the virtual machine concurrently communicates directly with both at least one of the plurality of nested hypervisors and the privileged hypervisor.
 9. The computer system according to claim 1, wherein servicing of input/output requests of the virtual machine is partitioned at a device-level granularity among the plurality of nested hypervisors.
 10. The computer system according to claim 1, wherein the virtual machine executes a polling mode driver which receives communications from at least one of the plurality of nested hypervisors without interrupts.
 11. A method of operating computer system, comprising: providing a plurality of nested hypervisors, configured to concurrently execute under a privileged hypervisor on at least one hardware processor, each of the plurality of nested hypervisors having a respective set of execution privileges on the at least one hardware processor lower than a set of execution privileges of the privileged hypervisor on the at least one hardware processor; and a virtual machine configured to concurrently communicate with the plurality of nested hypervisors, and having a memory map comprising a mapping of a plurality of logical memory page addresses having a common host physical memory address translated through each of at least two of the plurality of nested hypervisors; executing a first process of the virtual machine through a first hypervisor of the plurality of nested hypervisors having a first set of execution privileges and translating a host physical address of at least one memory page to a logical memory page address; and executing a second process of the virtual machine through a second hypervisor of the plurality of nested hypervisors having a second set of execution privileges and translating the host address of the at least one memory page to the logical memory page address.
 12. The method according to claim 11, wherein each of the plurality of nested hypervisors provides at least one service or resource to the virtual machine which is different from another of the plurality of nested hypervisors.
 13. The method according to claim 11, further comprising operating at least two of the plurality of nested hypervisors with respectively different sets of execution privileges.
 14. The method according to claim 11, further comprising exercising exclusive control over at least one portion of a memory image associated with the virtual machine by a single nested hypervisor of the plurality of nested hypervisors.
 15. The method according to claim 11, wherein at least one respective virtual central processing unit of the virtual machine executes under the plurality of nested hypervisors, and the plurality of nested hypervisors partition responsibility for scheduling the at least one respective virtual central processing unit.
 16. The method according to claim 11, wherein input-output scheduling for a first of the plurality of nested hypervisors is handled by a second of the plurality of nested hypervisors.
 17. The method according to claim 11, further comprising providing a second virtual machine, the virtual machine and the second virtual machine each relying on at least two concurrently available ones of the plurality of nested hypervisors to interface with the privileged hypervisor.
 18. The method according to claim 11, wherein the virtual machine concurrently communicates directly with both at least one of the nested hypervisors and the privileged hypervisor.
 19. The method according to claim 11, further comprising partitioning among the plurality of nested hypervisors, a servicing of input/output requests of the virtual machine at a device-level granularity.
 20. A computing system supporting multiple hypervisors, comprising: a unitary host machine having at least hardware one central processing unit and physical memory organized as memory pages; a plurality of hypervisors providing at least one of different privileges and different services, executing on the unitary host machine; and a plurality of virtual machines, each virtual machine being configured to concurrently execute on the plurality of hypervisors, each virtual machine having a memory image comprising at least one logical memory page having a common host physical memory address translated through each of the plurality of hypervisors, to thereby support execution of processes of each virtual machine dependent on the memory image through the plurality of hypervisors which provide the at least one of different privileges and different services. 